2020/04/01 by Johannes M. van Hulst, Mattia Zeni, van Hulst, Johannes M. +7
Computer Science · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2004.00491
openalex publication_date 2020/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the exponential advancement of business technology in recent years, data-driven decision making has become the core of most industries. With the rise of new privacy regulations such as the General Data Protection Regulation in the European Union and the California Consumer Privacy Act in the United States, companies dealing with personal data had to conform to these changes and adapt their processes accordingly. This obviously included the transportation industry with their use of location data. At the other side of the spectrum, users still expect a form of personalization, without having to compromise on their privacy. For this reason, companies across the industries started applying privacy-enhancing or preserving technologies at scale in their products as a competitive advantage. In this paper, we describe how Federated Machine Learning can be applied to the transportation sector. We present use-cases for which Federated Learning is beneficial in transportation and the new product lifecycle that is required for using such a technology. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy and one that guides us beyond privacy-regulations and into the world of ethical data-usage.